Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe
FRIOUX, Clémence
Pleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
< Reduce
Pleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE]
Language
en
Article de revue
This item was published in
Genome Research. 2023-06, vol. 33, p. 972 - 987
Cold Spring Harbor Laboratory Press
English Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and ...Read more >
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.Read less <
English Keywords
genomics
metabolism
metabolic evolution
genomes
systems biology
Origin
Hal imported